The systematic development of engineered microbial strains for optimizing the production of chemicals or biochemicals is an overarching challenge in biotechnology (Stephanopoulos et al., 1998). However, in the absence of metabolic and genetic engineering interventions, the product yields of many microorganisms are often far below their theoretical maximums. This is expected because cellular metabolism is primed, through natural selection, for the maximum responsiveness to the history of selective pressures rather than for the overproduction of specific chemical compounds. Not surprisingly, the behavior of metabolic networks is governed by internal cellular objectives which are often in direct competition with chemical overproduction targets.
The recent explosion of annotated sequence information along with a wealth of chemical literature has enabled the reconstruction of genome-scale metabolic networks for many microorganisms (Edwards and Palsson, 2000; Schilling and Palsson, 2000; Schilling et al., 2002; Forster et al., 2003). This information, used in the context of the flux balance analysis (FBA) modeling framework (Varma and Palsson, 1993), has been employed extensively to explore the integrated functions of metabolic networks (Burgard and Maranas, 2001; Burgard et al., 2001; Papin et al., 2003; Price et al., 2003). FBA models typically invoke the optimization of a particular cellular objective (e.g., ATP production (Majewski and Domach, 1990; Ramakrishna et al., 2001), biomass formation (Varma and Palsson, 1993,1994), minimization of metabolic adjustment (Segre et al., 2002)), subject to network stoichiometry, to suggest a likely flux distribution. Stoichiometric models of Escherichia coli (E. coli) metabolism utilizing the biomass maximization hypothesis have been in some cases successful at (i) predicting the lethality of gene knockouts (Edwards and Palsson, 2000; Badarinarayana et al., 2001), (ii) identifying the correct sequence of byproduct secretion under increasingly anaerobic conditions (Varma et al., 1993), and (iii) quantitatively predicting cellular growth rates under certain conditions (Edwards et al., 2001). Interestingly, recent work suggests that even when FBA predictions under the biomass maximization assumption seem to fail, metabolic networks can be evolved, for certain cases, towards maximum growth (i.e., biomass yield) through adaptive evolution (Ibarra et al., 2002).
The ability to investigate the metabolism of single-cellular organisms at a genomic scale, and thus systemic level, motivates the need for novel computational methods aimed at identifying strain engineering strategies.
Thus, one object, feature, or advantage of the present invention is to provide a method for computationally suggesting the manner in which to achieve bioengineering objectives.
A further object, feature or advantage of the present invention is to determine candidates for gene deletion or addition through use of a model of a metabolic network.
A still further object, feature or advantage of the present invention is to provide an optimized method for computationally achieving a bioengineering objective.
Yet another object, feature or advantage of the present invention is to provide an optimized method for computationally achieving a bioengineering objective that is robust.
One or more of these and/or other objects, features and advantages of the present invention will become apparent after review of the following detailed description of the disclosed embodiments and the appended claims.